Optimizing Use of AI
Targeting AI to industry-specific use cases will unleash more powerful innovation opportunities.
The use of AI among organizations has largely been limited to basic tasks, such as analyzing data, simplifying business processes, and performing quality assurance.
Already, though, we see organizations applying AI toward advanced tasks that are most relevant to their industries. For example, 85% of organizations in the data, AI, and cloud computing industry have used AI for automated data cleaning, while 74% of organizations in the energy solutions industry have used AI to forecast energy demand.
Developing targeted AI applications that are most relevant for their industries will help organizations capture more opportunities for AI-driven innovation and business transformation.
Top AI use cases by industry
73% of
Wireless 5G
Network monitoring and fault detection
74%
of
Energy
Forecasting demand
69% of
Auto/Commercial Transportation
Vehicle development and design
71%
of
Industrial Manufacturing
Predictive maintenance systems
85% of
Data, Cloud Computing, and AI
Automated data cleaning
TE Takeaway
An industrial predictive maintenance system uses AI and real-time sensor data to predict when equipment or machinery will need maintenance, preventing costly breakdowns and downtime. These systems analyze historical records and continuously monitor equipment health through IoT integrations, enabling them to detect early signs of failure. As AI technology advances, machine learning algorithms improve in accuracy, learning from past data to make smarter predictions. AI's growing capabilities allow these systems to optimize maintenance schedules, prioritize tasks based on risk, and enable faster responses to issues, leading to more efficient, cost-effective and reliable operations across industries.
Thomas Schoepf
Vice President and CTO
Industrial Automation & Electrification